{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization Tutorial 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Setup the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required library functions\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data, skip the top 20 and bottom 2 rows as they do not contain relevant data\n",
    "df_canada = pd.read_excel('data/canada.xlsx',\n",
    "                          sheet_name = 'Canada by Citizenship',\n",
    "                          skiprows = range(20),\n",
    "                          skipfooter = 2)\n",
    "\n",
    "# conversion index and columns to lists\n",
    "df_canada.columns.tolist()\n",
    "df_canada.index.tolist()\n",
    "\n",
    "# remove unnecessary columns\n",
    "# in pandas axis=0 re|presents rows (default) and axis=1 represents columns.\n",
    "df_canada.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# rename some columns to make better sense\n",
    "df_canada.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
    "\n",
    "# convert all column names to strings\n",
    "df_canada.columns = list(map(str, df_canada.columns))\n",
    "\n",
    "# full range of the time series\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "\n",
    "# add Total column\n",
    "df_canada['Total'] = df_canada.sum(axis=1)\n",
    "\n",
    "# index data by country\n",
    "df_canada.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Correlation (2 variables)\n",
    "\n",
    "### Visualize total immigration into Canada from 1980 to 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_total = pd.DataFrame(df_canada[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_total.index = map(int, df_total.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_total.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data points\n",
    "df_total.plot(kind='scatter',\n",
    "              x='year',\n",
    "              y='total',\n",
    "              figsize=(12, 6)\n",
    "             )\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute and Visualize Regression Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute line fit y = ax + b\n",
    "x = df_total['year']      # year on x-axis\n",
    "y = df_total['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f9F1WOnf2izzbtAGzsCOMOyXnIiIiIhKujRthzBhfurJwIdSqBXfe6evI65aS/urFRMm5iIiIiIRj0SKfkI8e7RP0E0+EsWPh8sshJcHaNpYQJeciIiIiUnIiEXj7bV+68u67kJzsk/G+feHkk8OOLnRKzkVEREQk/tatg+eegyFD4McfoU4duPdeuOEGOPTQsKNLGErORURERCR+5s3zpStjx8KWLX5h5/33w5/+5GfNZRdKzkVERESkeO3cCa+/7ktXpk/39eNXXQV9+kDLlmFHl9CUnIuIiIhI8cjMhJEjfX/yZcugXj0YOBCuvx7S0sKOrlRQci4iIiIi+2fOHD9L/uKLsG0bnHEGPP44dOwIFZVuFoWuloiIiIgUXU4OvPqqT8r/+1844AC49lpfunLccWFHV2opORcRERGR2K1eDcOHw7Bh8Msv0LAhPPIIdO8ONWuGHV2pp+RcRERERPbuiy/8LPnEibB9O5x3HjzzDLRvDxUqhB1dmaHkXEREREQKlp0Nkyb5pPyLL6BqVejZE26+GRo3Dju6MknJuYiIiIjs6pdffNnKM8/AmjVw1FHw5JPQtSscdFDY0ZVpSs5FREREBJyD//3P3zBo8mTfq/yCC6BvXzj3XEhKCjvCckHJuYiIiEh5tnUrjB/vS1fmzIHq1X1CfvPNcMQRYUdX7ig5FxERESmPli3zNwsaOdLfPKhpU1/Kcs01cOCBYUdXbik5FxERESkvnIMZM/ws+ZQpfuzii/1M+RlngFmY0QlKzkVERETKvs2b4fnnfT35d9/BwQfD//0f3HQTHHZY2NFJFCXnIiIiImXVkiUwZAg8+yysWwctWsCoUdClC1SpEnZ0UgAl5yIiIlIqZG7KJiNrK+k1q5BaNSXscBJXJALvv+9LV958098gqHNnX7py6qkqXQkk6vtJybmIiIgkvClzVtB/8lySk5LIiUQY1LkZHVvUDTusxLJhA4wd60tXFi6EWrXgn/+EG2+EumXjWhVXQp3I7ycl5yIiIpLQMjdl03/yXLblRNhGBIB+k+fSplFaQs14hmbhQp+QjxkDGzfCSSfBuHFw2WWQUnauT3El1In+flI3eREREUloGVlbSc53A5zkpCQysraGFFECiETgjTegXTto3NjfyfPii2HmTP+45poylZhHJ9Qbs3ewLSdCv8lzydyUXeRjJfr7STPnIiIiktDSa1YhJxLZZSwnEiG9Zjlc0LhunV/cOWSIX+z5hz/AvfdCz55Qu3bY0cVNbkKdO9MNvyfURZ3tTvT3k2bORUREJKGlVk1hUOdmVE5OolpKRSonJzGoc7OEKEEoMd99B716+drxv/3NJ+UvvQQ//wx33VWmE3Mo3oQ60d9P5pwLO4aE0KpVKzdr1qywwxAREZFCJGp3jbjZsQNefx2efNLfOKhyZbjqKujTB44/PuzoStzUOSvoV4yLOMN8P5nZbOdcq4K2qaxFRERESoXUqinlIyn/7TcYORKefhqWLYP69eHBB+H66yE1NezoQtOxRV3aNEortoQ6Ud9PSs5FREREEsHXX/ve5OPHw7ZtcOaZ8J//wEUXQUWlbJC4CXVx0k9aREREJCw5OfDKKz4p/9//4IADoFs3X7rStGnY0UkI4rYg1MzqmdmHZrbAzOaZ2a3B+L/MbIWZzQkeF0Tt83cz+8HMFppZu6jxE8zs22Dbk2b+1lZmlmJmLwXjM82sQdQ+Xc1scfDoGq/XKSIiIlJkq1f7LiuHHQZXXgmrVsGjj8KKFb6cRYl5uRXPmfMdwN+cc1+ZWTVgtplNC7Y97px7JPrJZtYEuBJoCvwBeN/MjnLO7QSeBnoCnwNvAecDbwM9gCznXCMzuxJ4CLjCzA4G7gFaAS4491TnXFYcX6+IiIjIns2c6WfJJ070s+bt2sGIEdC+PSSpiZ7EcebcObfSOfdV8PVGYAGwpyW1FwMTnHPZzrmfgB+Ak8ysDnCQc+4z51vLjAUuidpnTPD1y8DZwax6O2Cac25tkJBPwyf0IiIiIiUrO9vfsfOkk6B1a5g61bdF/P57eOcd6NChWBPzzE3ZfLN83T7doEfCVyI150G5yfHATKAN0MfMrgVm4WfXs/CJ++dRu2UEYznB1/nHCf5cDuCc22Fm64HU6PEC9omOqyd+Rp769evvz0sUERER2dWKFTBsGAwfDmvWwNFH+1nzrl2hWrW4nLK4bnEv4Yn770/MrCowGfiLc24DvkTlCKAFsBJ4NPepBezu9jC+r/v8PuDccOdcK+dcq0MOOWSPr0NERETKhrjOLDsH//0vXHEFNGgA998PJ58M770HCxb4hZ5xSsyL8xb3Ep64zpybWTI+MX/BOfcKgHNuddT2EcAbwbcZQL2o3dOBX4Lx9ALGo/fJMLOKQHVgbTB+Rr59ZhTHaxIREZHSK24zy1u3wosvwlNPwZw5UKMG3Hor9O4Nhx++/8ePQXHe4l7CE89uLQaMAhY45x6LGq8T9bROwHfB11OBK4MOLA2BI4EvnHMrgY1m1jo45rXAlKh9cjuxXApMD+rS3wXOM7OaZlYTOC8YExERkXIqLjPLS5dC//6Qnu5vErRjBzzzDGRkwCOPlFhiDsV7i/vipjr42MVz5rwN8GfgWzObE4z9A+hiZi3wZSY/AzcCOOfmmdlEYD6+08vNQacWgJuA0UAVfJeWt4PxUcA4M/sBP2N+ZXCstWZ2H/Bl8Lx7nXNr4/Q6RUREpBQotpll5+DDD339+NSpYAaXXAJ9+8Lpp/vvQ5BaNYVBnZvtdov7sGfNVQdfNOYnmqVVq1Zu1qxZYYchIiIicZK5KZs2D01nW87vyXnl5CT+1/+s2BLYTZvg+ed96cq8eZCWBjfc4DuvJFBjicxN2cV2i/viiGW/rnkZZWaznXOtCtqmhpoiIiJSLuTOLFdOTqJaSkUqJyfFNrP8449w222+dOWmmyAlBZ57DpYvhwceSKjEHPzrbF6vRkIkv7m/rYiW+9sKKViJtFIUERERSQQdW9SlTaO0vc8sRyK+w8pTT8Fbb0GFCnDppb505ZRTir10JZFmu4tTItfBJyol5yIiIiErq4lZokqtmlL4dd6wAUaPhiFDYNEiqF0b7r4bbrwR6tQpeJ/9VJZrshO1Dj6RKTkXEREJUVlOzEqV77/3s+Rjxvja8tat4YUX/Gx5pUpxO210B5nchar9Js+lTaO0MpPAxvzbCgGUnIuIiISmPCRmCW3nTl+yMngwTJvmk/Arr/SlK60KXKtX7MpLb/I9/rZCdqHkXEREJCTlJTFLOFlZ8OyzvnTlp5+gbl34979955VatUo0FNVkS37q1iIiIhISJWYl7Ntvfe14ejrcfjvUqwcTJ/oE/c47Szwxh/3oICNllmbORUREQqLFciVgxw6YMsXXk8+YAZUrw9VX+9KV5s3Djg5QTbbsSsm5iIhIiJSYxclvv8GIEfD0074f+WGHwaBB0KMHHHxw2NHtRjXZkkvJuYiISMiUmBWjr77yCzzHj4fsbDj7bP/9hRf6XuUiCU7JuYiIiJRuOTkwebJPwj/9FA48ELp3hz59oEmTsKMTKRIl5yIiIlI6rVoFw4fDsGGwciU0agSPPw7dukGNGmFHJ7JPlJyLiIhI6eEczJzpZ8knTfKz5u3bw8iRcP75kKRGdFK6KTkXERGRxJedDS+95JPyWbPgoIOgd2+4+WY48siwoxMpNkrORUREJHFlZPiyleHD4ddfoXFjf/OgP/8ZqlULOzqRYqfkXERERBKLc/Df//pZ8ldegUgELrrI9yY/+2wwCztCkbhRci4iIiKJYcsWePFFf8Ogb76BmjXhr3/15SsNG4YdXYEyN2WrR70UKyXnIiIiEld7TWB//hmGDoVRo2DtWmjWzN9A6Kqr4IADSjzeWE2Zs4L++e7u2rFF3bDDklJOybmIiIjETaEJrHMwfbovXXn9dV+q0qmTL11p2zbhS1cyN2XTf/JctuVE2EYEgH6T59KmUZpm0GW/KDkXERGRuCgogf3X+Jmc9WEGVUcMgwULIC0N7rgDevWCevVCjjh2GVlbSU5KyntdAMlJSWRkbVVyLvtFybmIiIjERXQCe1jWL3Sd/QaXffc+VbO3wAknwOjRcMUVULly2KEWWXrNKuREIruM5UQipNesElJEUlYoORcREZG4SK+eQutFM7nqi6mcuWQ2OUkVePeY02j7xACqn3V6wpeu7Elq1RQGdW5Gv3wlO5o1l/2l5FxERESK1/r1MHo0qUOGMGLxYtZUrcnQ069m/PHt+b9uZ1C9jCya7NiiLm0apalbixQrJeciIiJSPBYs8G0Qx4yBzZvhlFNgwAAqtLuQNpt3ckUZTGBTq6aUudck4VJyLiIiIvtu5054803fdeX996FSJejSxXddOeEEAFKB1IPDDVOktFByLiIiIkW3di08+ywMGeL7lKenw/33ww03wCGHhB2dSKml5FxERERiN3euL115/nnYuhVOPx0efhguuQQqKq0Q2V/6WyQiIiJ7tmMHvPaaL135+GOoUgWuvhr69IHmzcOOTqRMUXIuIiIiBfv1VxgxAp5+GjIyoEEDP0vevTscrCJykXhQci4iIlKGZG7K3v/WfrNn+1nyCRMgOxvOOcfXlnfoABUqFG/AIrILJeciIiJlxJQ5K+if76Y4HWPtKb59O0ye7JPyzz6DAw+EHj186coxx8Q3cBHJo+RcRMqVYplVjMOxRPZX5qZs+k+ey7acCNvwt5XvN3kubRql7fn9uWoVPPMMDBvmv27UCP7zH+jWDapXL5ngRSSPknMRKTf2a1YxjscSKQ4ZWVtJTkrKS8wBkpOSyMjaunty7hx8/rmfJX/5ZcjJgfbtfW/ydu0gKamEoxeRXPrbJyLlQvSs4sbsHWzLidBv8lwyN2WHeiyR4pJeswo5kcguYzmRCOk1q/w+sG2bv3vniSfCqaf6mwf17g2LFsFbb/kEXYm5SKj0N1BEyoXcWcVoubOKYR5LpLikVk1hUOdmVE5OolpKRSonJzGoczM/a56RAXfeCfXq+XKVLVtg6FBYscKXsBx5ZNjhi0hAZS0iUi7ENKsYwrFEilPHFnVp0yjNr4WoUZnUr7+Ay/4Cr77qS1kuusiXrpx1FpiFHa6IFEAz5yJSLuxxVjHEY4kUt9SknTR/92VSTzsZ/vhH+OADuO02+PFHfyOhs89WYi6SwMw5F3YMCaFVq1Zu1qxZYYchInGmbi1SZv30ky9VGTUKsrL8nTv79oUuXeCAA4p8OL2/ReLHzGY751oVtE1lLSJSrqRWTSm2RKM4jyWlU+gJrHN+ZnzwYHj9db+Ys1MnuOUWOO20fZ4hVzcikfAoORcREdkHoSawGzfCuHHw1FOwYAEccgj84x/Qqxekp+/Xofe5X7qIFAsl5yIiIkUUWgK7eLFPyEePhg0boFUr3xrx8suhcuViOUWR+qWLSLFTci4iIlJEJZrARiLwzju+dOWddyA5GS67zNeTn3xysS/uVDcikXCpW4uIiEgRlUgCu36970F+9NHQoQN88w0MGADLlsELL0Dr1nHpuqJuRCLh0sy5iIhIEeUmsP3y1ZwXSwI7f74vXRk7FjZv9nfyvO8++NOfoFKl/T9+DHbpl65uLSIlSsm5iIjIPijWBHbnTnjjDV+68sEHkJLiWyD27QstWxZf0EWgbkQi4dhrcm5mRwAZzrlsMzsDaAaMdc6ti3dwIiIiiWy/E9jMTN+XfOhQWLoU6tWDgQPh+ushLa34AhWRUiOWmvPJwE4zawSMAhoCL8Y1KhERkbLsm298Ap6eDv37Q8OGMHkyLFkCd9yhxFykHIulrCXinNthZp2A/zjnBpvZ1/EOTEREpEzJyYHXXvOlK598AlWqwLXXQp8+cNxxYUcnIgkiluQ8x8y6AF2Bi4Kx5PiFJCIiUoasWQMjRsDTT8OKFX6W/JFHoHt3qFkz7OhEJMHEkpxfB/QC7nfO/WRmDYHn4xuWiIhIKffll36W/KWXYPt2OPdcn6BfcAFUqBB2dCKSoGJJzs91zt2S+02QoG+NY0wiIiKl0/btMGmST8pnzoSqVeGGG3zpSuPGYUcnIqVALAtCuxYw1q2Y4xAR2aPMTdl8s3wdmZuyww5FZHe//FfDoSEAACAASURBVAL33AP168M118DatfDEE76M5amnlJiLSMwKnTkP6syvAhqa2dSoTdWAzHgHJiKSa8qcFfTPd7OXji3qhh2WlHfOwaef+lnyyZN9r/L27X1v8vPOgyTdhFtEim5PZS2fAiuBNODRqPGNwNx4BiUikitzUzb9J89lW06EbfjbpfebPJc2jdJ0gxQJx7ZtMH68T8q//hqqV/cJee/e0KhR2NGJSClXaHLunFsKLAVOKblwRER2lZG1leSkpLzEHCA5KYmMrK1KzqVkLVvmF3SOGOFvHtSkib950J//7GvLRUSKQSx3CP0T8BBQC7Dg4ZxzB8U5NhER0mtWIScS2WUsJxIhvWaVkCKScsU5+OgjP0v+2mt+rGNHP1N+5plgFm58IlLmxFIQNwjo6Jyr7pw7yDlXTYm5iJSU1KopDOrcjMrJSVRLqUjl5CQGdW6mWXOJr82bYfhwaNbMJ+EzZsDtt8OPP8Krr8JZZykxF5G4iKWV4mrn3IK4RyIiUoiOLerSplEaGVlbSa9ZRYm5xM+SJb5UZdQoWLcOmjeHkSPhqqv8HT1FROIsluR8lpm9BLwG5PUwc869EreoRETySa2aoqRc4sM5mDbNl668+abvstK5sy9dadNGM+QiUqJiSc4PArYA50WNOUDJuYiIlF4bN8KYMb4P+cKFUKsW3Hkn9OoFddWqU0TCsdfk3Dl3XUkEIiIiUiIWLfIJ+ejRPkE/8UQYOxYuvxxS9NsZEQnXXheEmlllM7vZzIaa2bO5jxj2q2dmH5rZAjObZ2a3BuMHm9k0M1sc/Fkzap+/m9kPZrbQzNpFjZ9gZt8G2540879jNLMUM3spGJ9pZg2i9ukanGOxmRV0l1MRESkvIhFfsnL++XD00TBsGFx8McycCV984dshKjEXkQQQS7eWccChQDvgIyAdfyOivdkB/M05dwzQGrjZzJoAdwAfOOeOBD4IvifYdiXQFDgfGGpmFYJjPQ30BI4MHucH4z2ALOdcI+BxfMtHzOxg4B7gZOAk4J7oDwEiIrJnmZuy+Wb5OjI3Ze/9yYls3Tp4/HE46ii48EL49lu4915YvhzGjYOTTgo7QhGRXcSSnDdyzt0FbHbOjQE6AMftbSfn3Ern3FfB1xuBBUBd4GJgTPC0McAlwdcXAxOcc9nOuZ+AH4CTzKwOcJBz7jPnnAPG5tsn91gvA2cHs+rtgGnOubXOuSxgGr8n9CIisgdT5qygzUPTuWbkTNo8NJ2pc1aEHVLRzZsHN93ka8dvuw0OPRQmTICff4a77oLatcOOUESkQLEsCM0J/lxnZscCq4AGRTlJUG5yPDATqO2cWwk+gTezWsHT6gKfR+2WEYzlBF/nH8/dZ3lwrB1mth5IjR4vYB8RESlE5qZs+k+ey7acSN5dWftNnkubRmmJ3y1n5054/XXfdWX6dF+mctVVvuvK8ceHHZ2ISExiSc6HByUh/wSmAlWBu2I9gZlVBSYDf3HObbDCW1IVtMHtYXxf94mOrSe+XIb69esXFpeISLmRkbWV5KSkvMQcIDkpiYysrYmbnGdm+l7kQ4fCsmVQvz48+CD06AFpaWFHJyJSJHtMzs0sCdgQlIZ8DBxelIObWTI+MX8hqi/6ajOrE8ya1wHWBOMZQL2o3dOBX4Lx9ALGo/fJMLOKQHVgbTB+Rr59ZuSPzzk3HBgO0KpVq92SdxGR8ia9ZhVyIpFdxnIiEdJrJuANeObM8bPkL74I27b5O3n+5z9w0UVQMZa5JxGRxLPHmnPnXATosy8HDmq/RwELnHOPRW2aCuR2T+kKTIkavzLowNIQv/Dzi6AEZqOZtQ6OeW2+fXKPdSkwPahLfxc4z8xqBrP+5wVjIiKyB6lVUxjUuRmVk5OollKRyslJDOrcLHFmzXNyYOJEaNvWl6pMmABdu/qFntOnQ6dOSsxFpFSL5V+waWZ2O/ASsDl30Dm3di/7tQH+DHxrZnOCsX8ADwITzawHsAy4LDjePDObCMzHd3q52Tm3M9jvJmA0UAV4O3iAT/7HmdkP+BnzK3NjM7P7gC+D590bQ7wiIgJ0bFGXNo3SyMjaSnrNKomRmK9ZA8OH+xaIK1bA4YfDo4/CdddBTTXjEpGyw/xE8x6eYPZTAcPOOVekEpdE16pVKzdr1qywwxARkWhffulLV156CbZvh/PO8ws827eHChX2vr+ISAIys9nOuVYFbYvlDqENiz8kERGRQmRnw6RJ/i6eM2dC1arQsyf06eNvICQiUobtNTk3sz8VMLwe+NY5t6aAbSIiUgSZm7ITq4QkLL/84stWhg+H1av9jYOefNLXlB90UNjRiYiUiFhqznsApwAfBt+fge9HfpSZ3eucGxen2EREyrwpc1bQf/JckpOSyIlEGNS5GR1blJ3bMuz1g4dz8OmnvnRl8mTfq7xDBz9Lfu65kBTLvfJERMqOWJLzCHCMc241gJnVBp4GTsa3V1RyLiKyD0r1DX9isMcPHlu3+k4rgwfD119D9epwyy3QuzcccUS4gYuIhCiW5LxBbmIeWAMcFXREySlsJxER2bNSecOfGBX2waNtyhZqjhnlbxqUmQlNm/pSlmuugQMPDDlqEZHwxZKcf2JmbwCTgu87Ax+b2YHAurhFJiJSxpWqG/4U0S4fPJzjlGXf0mPOG9R44HP/hIsv9l1XzjgDCr9ztIhIuRNLcn4zPiFvAxgwFpgc3OznzDjGJiJSpuXe8KdfvtKP0j5rDv6DR8VtW7jqmw+4dvYbNP5tKeuqVGPbrbdR5dY+cNhhYYcoIpKQ9trnvLxQn3MRCUuZ69ayZAkMGcL2EaOotHE9Cw49gnGtLuLUO2/mwtaNwo5ORCR0+9Tn3Mz+65w7zcw2AtEZvOFvQqS+ViIixSC1akrpT8ojEZg2zS/wfOstqFCBSp07s77HjWw/ugV/O/iA0v8aRURKQKHJuXPutODPaiUXjoiUJWVuRrgUKPFrvmEDjBnjbxi0aBHUqgX//CfceCPUrUt1oHn8oxARKTNiqTnHzGoC9aKf75z7Kl5BiUjpV9b7dyeiEr3mCxf6hHzMGNi4EU46CcaNg8sugxR9EBMR2Vex3CH0PqAbsATy+n054Kz4hSUipVlZ79+diErkmu/cCW+/7UtX3nsPkpPhiit815WTTiqec4iIlHOxzJxfDhzhnNse72BEpGwoy/27E1Vcr/m6dfDsszBkiF/s+Yc/wL33Qs+eULv2fkYuIiLRYknOvwNq4G8+JCKyV2W5f3eiiss1/+47X7oybhxs2QKnnQYDB0KnTn7WXEREil1SDM8ZCHxtZu+a2dTcR7wDE5HSK7d/d+XkJKqlVKRyclKZ6d+dqIrtmu/YAa+8AmedBccd52vKr7wSvvoKPvkELr9cibmISBzttc+5mc0DngG+5feac5xzH8U3tJKlPucixU/dWkrePl/z336DkSPh6adh2TKoXx9694brr4fU1PgFLCJSDu1Tn/MovznnnizmmESkHCgT/btLmSJf86+/9gs8x4+Hbdv8jPkTT8BFF0GFCvELVEREChRLcj7bzAYCU4Hs3EG1UhQRKaVycnzpyuDB8L//wQEHQLdu0KcPNG0adnQiIuVaLMn58cGfraPG1EpRRKS0Wb0annkGhg2DlSvhiCPgscfguuugRo2woxMREWJIzp1zZ5ZEICIiEiczZ/pZ8okT/ax5u3YwYgS0bw9JsfQFEBGRkhLLTYhqANcCDdj1DqG3xC8sERHZL9nZPhkfPBi+/BKqVYObbvKLPI8+OuzoQqWFyiKSyGIpa3kL+Jx83VpERCQBrVjhy1aGD4c1a6BxY9+r/NprfYJezk2Zs4L+k+eSnJRETiTCoM7N6NiibthhiYjkiSU5r+ycuy3ukYiIyL5xzi/sHDzYL/TcuRMuvBD69oVzzgGzsCNMCJmbsuk/eS7bciJ5d1LtN3kubRqlaQZdRBJGLMn5ODO7AXiDXbu1rI1bVCIisndbt/oWiIMHw5w5flHnrbf60pXDDw87uoSTkbWV5KSkvMQcIDkpiYysrUrORSRhxJKcbwceBu7Ed2kh+FP/8ouIhGHpUhg61N80aO1afyfP4cPh6qt9W0QpUHrNKuREdq3OzIlESK9ZJaSIRER2F0tyfhvQyDn3W7yDERGRQjgHH37oZ8mnTvWlKpdc4ktXTj9dpSsxSK2awqDOzeiXr+Zcs+YikkhiSc7nAVviHYiIiBRg0yZ4/nm/qHPePEhLg/79feeVevXCjq7U6diiLm0apalbi4gkrFiS853AHDP7kF1rztVKUUQkXn78EYYMgWefhfXroWVLeO45uPJKqFw57OhKtdSqKUrKRSRhxZKcvxY8REQkniIReO89P0v+1ltQoQJceinccgu0bq3SFRGRciCWO4SOKYlARETKrQ0bYPRoP1O+aBHUrg133w033gh16oQdnYiIlKBCk3Mz+5bfu7PsxjnXLC4RiYiUF99/72fJx4zxteWtW8MLL/jZ8kqVwo5ORERCsKeZ8wtLLAoRkfJi505fsjJ4MEyb5pPwK6+EPn3gxBPDjk5EREJWaHLunFtakoGIiJRpWVl+ceeQIfDTT1C3Lvz733DDDVCrVtjRiYhIgohlQaiIiOyrb7/1s+TPP+/v6Hn66fDQQ75HeXJy2NGJiEiCUXIuIlLcduyAKVN8Uv7RR7714TXX+NKV5s3Djk5ERBJYUmEbzOyD4M+HSi4cEZFS7LffYOBAOPxwv6jz559h0CBYsQJGjFBiLiIie7WnmfM6ZvZHoKOZTQB2abDrnPsqrpGJiJQWX33lZ8nHj4fsbDj7bP/9hRf6XuUiIiIx2lNyfjdwB5AOPJZvmwPOildQIiIJb/t2eOUVn4R/+ikceCB07+5LV5o0CTs6EREppfbUreVl4GUzu8s5d18JxiQikrhWrYJnnvGPlSuhUSN4/HHo1g1q1Ag7OhERKeViuUPofWbWETg9GJrhnHsjvmGJiCQQ52DmTD9LPmkS5OTA+efDyJH+z6RCl++IiIgUyV6TczMbCJwEvBAM3WpmbZxzf49rZCIiYcvOhpde8kn5rFlQrRrcdBPcfDMcdVTY0YmISBkUSyvFDkAL51wEwMzGAF8DSs5FpGzKyIBhw2D4cPj1VzjmGH/zoD//2SfoIiIicRJrn/MawNrg6+pxikVEEkTmpmwysraSXrMKqVVTwg6nZDgHn3ziZ8lffRUiEbjoIujb13dfMdv7MURERPZTLMn5QOBrM/sQ307xdDRrLlJmTZmzgv6T55KclEROJMKgzs3o2KJu2GHFz5Yt8OKL8NRT8M03ULMm/PWv0Ls3NGwYdnQiIlLOxLIgdLyZzQBOxCfn/Z1zq+IdmIiUvMxN2fSfPJdtORG2EQGg3+S5tGmUVvZm0H/+GYYO9Ys6s7LguON8GcvVV8MBB4QdnYiIlFMxlbU451YCU+Mci4iELCNrK8lJSXmJOUByUhIZWVvLRnLuHEyf7ktXXn/dl6pccokvXTn9dJWuiIhI6GKtOReRciC9ZhVyIpFdxnIiEdJrVgkpomKyaROMHetLVxYsgLQ06N/fd16pVy/s6ERERPKoOa+I5EmtmsKgzs2onJxEtZSKVE5OYlDnZqV31nzxYvjLX6BuXd/+8IADYPRoWL4cHnhAiXkpk7kpm2+WryNzU3bYoYiIxM0eZ87NLAmY65w7toTiEZGQdWxRlzaN0kpvt5ZIBN5915euvP02VKwIl13mS1dat1bpSilV7hYqi0i5tcfk3DkXMbNvzKy+c25ZSQUlIuFKrZpS+pLy9ev9rPiQIX7G/NBD4Z574MYboU6dsKOT/VCuFiqLSLkXS815HWCemX0BbM4ddM51jFtUIiKxmj/f15KPHQubN8Mpp8CAAdC5M1SqFHZ0UgzK/EJlEZEosSTnA+IehYhIUezcCW+84UtXPvgAUlLgyit96coJJ4QdnRSzMrtQWUSkAHtdEOqc+wj4GUgOvv4S+CrOcYmI7G7tWnj4YWjUyLdAXLgQ7r/fL/AcPVqJeRlV5hYqi4jswV5nzs3sBqAncDBwBFAXGAacHd/QREQCc+f6WfIXXoCtW+GPf4RHHoGLL/YLPqXMK/ULlUVEYhTL/2o3AycBMwGcc4vNrFZcoxIR2bEDXnsNnnwSPvkEqlSBa66BPn2gWbOwo5MQlMqFyiIiRRRLcp7tnNtuQfsxM6sIuLhGJSLl16+/wvDhMGwYZGRAgwa+lKV7dzj44LCjExERiatYkvOPzOwfQBUzOxfoDbwe37BEpNyZNcuXrkyYANu3wznn+LaIHTpAhQphRyciIlIiYknO7wB6AN8CNwJvASPjGZSIlBPbt8PLL/uk/PPP4cAD4frrfenKMceEHZ2IiEiJi6VbSwQYA9yHb6s4xjmnshYR2XcrV8K//gWHHQZXXw2ZmfDEE7BihZ8tV2Iu5US3bt1o2LAhLVq0oEWLFsyZMweAGTNmUL169bzxe++9N2+fdevWcemll9K4cWOOOeYYPvvsMwCuuOKKvOc3aNCAFi1ahPKaunfvTq1atTj22F1vLr527VrOPfdcjjzySM4991yysrIA2L59O9dddx3HHXcczZs3Z8aMGQBs2bKFDh060LhxY5o2bcodd9yxy/EmTpxIkyZNaNq0KVdddVXe+Pnnn0+NGjW48MILd3n+9OnTadmyJcceeyxdu3Zlx44dwJ6vtUgonHN7fAAdgOXADOAjYBnQfm/7lbbHCSec4EQkjiIR5z791LkuXZyrWNE5cO6CC5x7+23ndu4MOzqRUHTt2tVNmjRpt/EPP/zQdejQocB9rr32WjdixAjnnHPZ2dkuKytrt+fcdtttbsCAAfsd33PPPefuueeeIu3z0UcfudmzZ7umTZvuMv5///d/buDAgc455wYOHOj69evnnHPuqaeect26dXPOObd69WrXsmVLt3PnTrd582Y3ffp055x/naeddpp76623nHPOLVq0yLVo0cKtXbs2b79c77//vps6deou12/nzp0uPT3dLVy40Dnn3F133eVGjhzpnNvztRaJF2CWKyQn3evMOfAocKZz7gzn3B+BM4HH4/JJQUTKnm3bYMwYOPFEOPVUePNNX7ayeLH/+vzzISmWf4pEEtNdd93FE088kff9nXfeyZNPPhmXc23YsIGPP/6YHj16AFCpUiVq1Kixy3Occ0ycOJEuXbrstv+rr77KOeecg3OOlStXctRRR7Fq1apijfH000/n4AIWb0+ZMoWuXbsC0LVrV1577TUA5s+fz9ln++7MtWrVokaNGsyaNYsDDjiAM888M+91tmzZkoyMDABGjBjBzTffTM2aNfP2y3X22WdTrVq1Xc6dmZlJSkoKRx11FADnnnsukydPLs6XLVJsYvkfcY1z7oeo75cAa+IUj4iUFcuXw513Qr160K0bbNkCQ4f60pXHH/c3EhIpA3r06MGYMWMAiEQiTJgwgauvvpqNGzfmlUrkf8yfPz9v/zvvvJNmzZrx17/+lezs7Lzxzz77jObNm9O+fXvmzZsHwJIlSzjkkEO47rrrOP7447n++uvZvHnzLvF88skn1K5dmyOPPHK3WDt16sShhx7KkCFDuOGGGxgwYACHHnpoPC7LblavXk2dOnUAqFOnDmvW+FSiefPmTJkyhR07dvDTTz8xe/Zsli9fvsu+69at4/XXX89L4hctWsSiRYto06YNrVu35p133tnjudPS0sjJyWHWrFkAvPzyy7uco6BrLRKWQheEmtmfgi/nmdlbwER8C8XL8HcJ3SMzexa4EJ/cHxuM/Qu4Afg1eNo/nHNvBdv+jl94uhO4xTn3bjB+AjAaqIJfjHqrc86ZWQowFjgByASucM79HOzTFfhncI5/O+fG7C1eESkGzsHHH/sFnq+95r+/6CLo2xfOOguClqwiZUmDBg1ITU3l66+/ZvXq1Rx//PGkpqYC5NWQF2bgwIEceuihbN++nZ49e/LQQw9x991307JlS5YuXUrVqlV56623uOSSS1i8eDE7duzgq6++YvDgwZx88snceuutPPjgg9x33315xxw/fnyBs+a5Bg8ezLHHHkvr1q0LfF5mZmZeErx27Vq2b9+eN8s9btw4jjvuuCJfoz3p3r07CxYsoFWrVhx22GGceuqpVIy6udiOHTvo0qULt9xyC4cffnje2OLFi5kxYwYZGRm0bduW7777brffIuQyMyZMmJD3Aei8887LO0dh11okLHvq1nJR1NergT8GX/8K1Izh2KOBp/AJdLTHnXOPRA+YWRPgSqAp8AfgfTM7yjm3E3gaf4fSz/HJ+fnA2/hEPss518jMrgQeAq4ws4OBe4BW+A8Ts81sqnMuK4aYRWRfbNni79751FP+bp41a8Jtt0Hv3r5PuUgZd/311zN69GhWrVpF9+7dAdi4cSNt27Yt8PkvvvgiTZo0yZtJTklJ4brrruORR/x/jwcddFDecy+44AJ69+7Nb7/9Rnp6Ounp6Zx88skAXHrppTz44IN5z92xYwevvPIKs2fPLjTWFStWkJSUxOrVq4lEIiTlKytLTU3N+1AxevRofv75Z/71r38VeKzly5dz0UU+XejVqxe9evUq9Ly1a9dm5cqV1KlTh5UrV+aVolSsWJHHH/+9WvbUU0/dZda/Z8+eHHnkkfzlL3/JG0tPT6d169YkJyfTsGFDjj76aBYvXsyJJ55Y6PlPOeUUPvnkEwDee+89Fi1aBBR+rdPS0go9lkg8FZqcO+eu258DO+c+NrMGMT79YmCCcy4b+MnMfgBOMrOfgYOcc58BmNlY4BJ8cn4x8K9g/5eBp8zfKakdMM05tzbYZxo+oR+/P69HRArw00++VGXUKMjKgubNYeRI6NIFDjgg7OhESkynTp24++67ycnJ4cUXXwSgWrVqe505z01WnXO89tpreR1OVq1aRe3atTEzvvjiCyKRCKmpqZgZ9erVY+HChRx99NF88MEHNGnSJO9477//Po0bNyY9Pb3A8+3YsYPrrruOF198kbFjx/LYY49x++237/Prrlev3l5fY66OHTsyZswY7rjjDsaMGcPFF18M+K4szjkOPPBApk2bRsWKFfNe0z//+U/Wr1/PyJG7dnC+5JJLGD9+PN26deO3335j0aJFebPqhVmzZg21atUiOzubhx56iDvvvBMo/FqLhGWvfc7NrCHQF2gQ/XznXMd9PGcfM7sWmAX8LZjRroufGc+VEYzlBF/nHyf4c3kQyw4zWw+kRo8XsE/+19YTPytP/fr19/HliJQzzsEHH/jSlddf94s5//QnX7py2mkqXZFyqVKlSpx55pnUqFGDCkW4adbVV1/Nr7/+inOOFi1aMGzYMMDXRD/99NNUrFiRKlWqMGHCBHLv1D148GCuvvpqtm/fzuGHH85zzz2Xd7wJEybssaTlgQceoG3btrRt25YWLVpw4okn0qFDB44pxvalXbp0YcaMGXkz/QMGDKBHjx7ccccdXH755YwaNYr69eszadIkwCfN7dq1Iykpibp16zJu3DgAMjIyuP/++2ncuDEtW7YEoE+fPlx//fW0a9eO9957jyZNmlChQgUefvjhvIS6bdu2fP/992zatIn09HRGjRpFu3btePjhh3njjTeIRCLcdNNNnHXWWXu91iJhMLeXluVm9g0wCn8TokjuuHPuo70e3M+cvxFVc14b+A1fbnIfUMc5193MhgCfOeeeD543Cl/CsgwY6Jw7JxhvC/Rzzl1kZvOAds65jGDbj8BJQHcgxTn372D8LmCLc+7RPcXaqlUrl7tQREQKsHEjjBvnS1cWLIBDDoGePaFXLyhklk6kvIhEIrRs2ZJJkyYVuBBTRCSamc12zrUqaFssdwjd5pwrlp5QzrnVUUGNAN4Ivs0A6kU9NR34JRhPL2A8ep8MM6sIVAfWBuNn5NtnRnHEL1IuLV7sE/LRo2HDBt8ScexYuOwyqFw57OhEQjd//nwuvPBCOnXqpMRcRPZbLMn5E2Z2D/AekNfjyTn3VVFPZmZ1nHMrg287Ad8FX08FXjSzx/ALQo8EvnDO7TSzjWbWGpgJXAsMjtqnK/AZcCkwPeji8i7wgJnlLlo9D/h7UWMVKdciEXjnHV+68s47kJwMl1/uS1eChWgi4jVp0oQlS5aEHYaIlBGxJOfHAX8GzuL3shYXfF8oMxuPn8FOM7MMfAeVM8ysRbD/z8CNAM65eWY2EZgP7ABuDjq1ANzE760U3w4e4EttxgWLR9fiu73gnFtrZvfxe7vHe3MXh4rIXqxfD889B0OGwA8/QJ06MGCAL18poV7IIiIi5VksNeffA82cc9tLJqRwqOZcyrX5833pytixsHmzv5Nn375+oWelSmFHJyIiUqbsb835N0ANdFdQkbJl507fbeWpp3z3lZQU3wKxb18IOiOIiIhIyYolOa8NfG9mX7Jrzfm+tlIUkTBlZvq+5EOHwtKlUK8eDBwI118PuumGiIhIqGJJzu+JexQiEn/ffOMXeL7wAmzbBmecAY89Bh07QsVY/ikQERGReNvr/8ix9DMXkQSVkwOvveaT8k8+8XftvPZa6NMHjjsu7OhEREQkn1juELoR310FoBKQDGx2zh0Uz8BEZD+sWQMjRsDTT8OKFXD44fDoo3DddVCz5t73FxERkVDEMnNeLfp7M7sEfydOEUk0s2bBk0/CSy/B9u1w3nkwbBi0b///7d17lJXVff/x9/fIOCCjBofoT0GMxlu9IBpCVaKx0So23iKaEPUnJa5qvDVpuyqmrT9XY7qqpElsvEXiDbRewf40jabRRGs0/iCYImq8RqMOEqgwKIMwDJz9++N5Jg7IZQbO5ZmZ92utszjsc55nvmdnj/mwz372Az24pbgkSaqPHi80TSn934i4tBrFSNoMq1bBfffRcfX3aZgzm9TURJx7Llx4Iey7b72rkyRJPdCdZS2ndvlrCRjNh8tcJNXLyvFdOAAAFeZJREFUO+/AjTdmj4ULadlhGHePO58ZBx7N5Wccykn7Dqt3hZIkqYe6M3N+Ypfnq8nu7HlyVaqRtHEpwdNPZxd4zpgBa9aw6thxXLDD4fxs14NIUQLgkpnzGLvnUJqbGutcsCRJ6onurDmfVItCJG3EypVw991ZKP/1r2H77bObBV14IS9u3cysm2aR2lf/4e0NpRItrSsM55Ik9TIbDOcR8X82clxKKV1RhXokdfXWW9kFnT/8Ibz7Luy/f/b3s86CwYMBGN7WTke5vNZhHeUyw4cMqkfFkiRpC5Q28try9TwAzgEmV7kuqf9KCR5/HMaPh913h6uugiOOgJ//HJ57Ds477w/BHKC5qZEp40cysKHEto0DGNhQYsr4kc6aS5LUC21w5jyl9J3O5xGxLfA1YBJwN/CdDR0naTMtX57dvfPaa7MQvsMO8Ld/C+efD7vtttFDTxo1jLF7DqWldQXDhwwymEuS1EttdM15ROwA/DVwJjANOCSl1FqLwqT+ovX5l+i45jqG3nsHpaVLYdQouPlm+PKXYVD3l6Y0NzUayiVJ6uU2tub828CpwFTgwJRSW82qkvq6lODRR1nwrW+z0xOPUo7gJ380lu2++1d85s9PgYh6VyhJkupgYzPnfwO0A/8A/H18GBaC7ILQ7apcm9T3LFsG06dnS1deeomGwR/j2sO+yL8dfDwLtx3KwNdLPLV8lTPgkiT1Uxtbc76xi0Ul9cQrr2SB/LbbsoA+ZgxvXv0DvrBoF5as+fBXzS0QJUnq3wzgUrWUy/DjH8O4cbDPPtkWiCefDLNmwaxZNJ3z53xQWvvfx26BKElS/2Y4lypt6VL43vdg773hhBOynVe++U14+224/XYYMwZwC0RJkvRRm7xDqKRueuGFbOnK9OnwwQcwdiz80z/BqadCQ8N6D3ELREmS1JXhXNoSq1fDj34E11wDjz0GjY1wxhlw8cVw8MHdOoVbIEqSpE6Gc2lzLF4MN90E118Pb70FI0bAlVfCOefA0KH1rk6SJPVShnOpJ+bOzWbJ77wTVq6EP/kTuPpqOPFEGOCvkyRJ2jKmCWlTOjrg3/89C+VPPgnbbAMTJ8JFF8EBB9S7OkmS1IcYzqUNWbgQpk7NtkB85x3YYw/4zndg0iQYMqTe1UmSpD7IcC6ta/bsbJb83nth1So47ji48UY4/njYaqt6VydJkvoww7kE0N4O992XhfLZs6GpCc49N1u6ss8+9a5OkiT1E4Zz9W/z52fLVqZOhUWLsiB+zTVw9tmw3Xb1rk6SJPUzhnP1PynBU09lIfz++2HNGvj857O9yY85BkreOFeSJNWH4Vz9x4oVcNddWSifOxe23x7+8i/hggvgk5+sd3WSJEmGc/UDb74JN9yQ3TRo8WLYf/9sKctZZ8HgwfWuTpIk6Q8M5+qbUoLHHoNrr4UHHsjaTjklu8DzqKMgoq7lSZIkrY/hXH3L8uVw++1ZKH/hBWhuhksugfPPhxEj6l2dJEnSRhnO1Tf89rdw3XVwyy3w3ntw8MHZ8wkTYNCgelcnSZLULYZz9V7lMjzySHaB50MPZTcIOu20bOnK4Ye7dEWSJPU6hnP1Pu+/D9OmZUtXXnkFdtoJLrsMzjsPdtml3tVJkiRtNsO5eo+XX84C+W23QVsb/PEfwx13ZLPljY31rk6SJGmLGc5VbGvWwMMPZ0tXfvpT2Hpr+NKXshsGffrT9a5OkiSpogznKqbW1uyCzuuvh9dfz5arXHEFnHsu7LhjvauTJEmqCsO5iuX557NZ8jvugA8+gCOOgCuvzPYob2iod3WSJElVZThX/a1eDQ8+mIXyxx+HgQPhzDOzXVdGjap3dZIkSTVjOFf9vPsu3HQT3HADvPUW7LYbXHUVnHNOdvMgSZKkfsZwrtr77//OZsnvvBPa2+Fzn4N//Vc48cRsr3JJkqR+ynCu2ujogPvvz0L5U0/BNtvApEnZ0pX99693dZIkSYVgOFd1LVwIN94IP/gBLFgAn/wkfPe7WTD/2MfqXZ0kSVKhGM5VHbNmZTcMuueebNZ83Lhsffm4cVAq1bs6SZKkQjKcq3La2+Hee7OlK7/6FWy7LZx/Plx4Iey9d72rkyRJKjzDeR+xuK2dltYVDB8yiOamGt/Kfv78bNnK1KmwaBHsu282a3722VlAlyRJUrcYzvuAB+bOZ/LMeTSUSnSUy0wZP5KTRg2r7g9NCZ58Mpslv/9+KJfhhBPg4ovhmGMgoro/X5IkqQ8ynPdyi9vamTxzHis7yqykDMAlM+cxds+h1ZlBX7Ei2wLx2mth7tzsos6vfx0uuAD22KPyP0+SJKkfMZz3ci2tK2golf4QzAEaSiVaWldUNpy/+SZcf312UeeSJXDAAdkuLGeeCYMHV+7nSJIk9WOG815u+JBBdJTLa7V1lMsMHzJoy0+eEjz2WLZ05cEHs6Uqp5ySLV058kiXrkiSJFWYe9r1cs1NjUwZP5KBDSW2bRzAwIYSU8aP3LJZ87Y2uOGGbHb86KPhF7+AyZPhjTdgxgz47GcN5pIkSVXgzHkfcNKoYYzdc+iW79by2mtw3XVw663w3ntwyCHZ8wkTYODAyhYtSZKkjzCc9xHNTY2bF8rLZfjpT7OlKw8/DFttBaedli1dOewwZ8glSZJqyHDeX73/Ptx2W7bryquvwk47wWWXwXnnwS671Ls6SZKkfslw3t+8+GK2dGXatGxt+aGHwuWXw+mnw9ZbA3W+oZEkSVI/ZjjvD9asgR//OFu68uijWQifMCFbujJ69FpvrcsNjSRJkgQYzvu21la4+eZsf/I33oBhw+Bb34K/+AvYccePvL3mNzSSJEnSWgznfdFzz2Wz5Hfckd3R88gj4aqrsj3KGxo2eFjNbmgkSZKk9TKc9xWrV8MDD2Sh/L/+K9v68Kyz4KKL4KCDunWKqt7QSJIkSZtUtZsQRcQtEbEoIp7v0rZDRDwSEa/mfw7p8to3IuK1iHg5Io7r0v6piHguf+37EdnefhHRGBH35O2zIuITXY6ZmP+MVyNiYrU+YyG8+y788z/DHntkWyD+7ncwZQrMnw8//GG3gzlU6YZGkiRJ6rZIKVXnxBFHAm3A9JTSAXnbFGBJSunKiLgUGJJSmhwR+wF3AWOAXYBHgb1TSmsiYjbwNeD/AQ8B308pPRwRFwAjU0pfjYgJwBdSSl+KiB2AOcBoIAHPAJ9KKbVurN7Ro0enOXPmVL4jquWZZ7JZ8rvvhvb27E6eF18MJ5yQ7VW+BdytRZIkqXoi4pmU0uj1vVa1mfOU0hPAknWaTwam5c+nAad0ab87pdSeUnoDeA0YExE7A9ullJ5O2b8ipq9zTOe5ZgBH57PqxwGPpJSW5IH8EWBc5T9hZSxua+fZt5eyuK19029etQruugsOPzzbZWXGDPjKV+CFF7JdWE4+eYuDOWQz6Aft+jGDuSRJUo3Ves35TimlBQAppQUR0bllyDCymfFOLXlbR/583fbOY97Oz7U6It4Dmru2r+eYQun2toW//z3ceGP2WLAA9twTvvc9mDQJtt++9oVLkiSpKopyQej67hGfNtK+uces/UMjzgXOBRgxYsSmq6ygTW5bmBLMmpUtXbnvPujogOOPh5tugnHjoFS1Lz0kSZJUJ7VOeAvzpSrkfy7K21uAXbu8bzjwTt4+fD3tax0TEQOA7cmW0WzoXB+RUpqaUhqdUhr98Y9/fAs+Vs91blvYVUOpxPzfL4Xp02HMGDjsMPiP/4ALLoBXXoGHHoI/+zODuSRJUh9V65T3INC5e8pE4IEu7RPyHVh2B/YCZudLYJZFxKH5evKz1zmm81ynAT/P16X/J3BsRAzJd4M5Nm8rlHW3Lfxf77/LBT+7lQMOOxAmToTly+G666ClBa6+Gvbaq47VSpIkqRaqtqwlIu4CjgKGRkQLcDlwJXBvRJwDvAWcDpBSeiEi7gV+A6wGLkwprclPdT5wGzAIeDh/ANwM3B4Rr5HNmE/Iz7UkIq4AfpW/75sppXUvTK275qZGppx6IPdcfRdnz/kRR7/4FFuRiBNPzHZdOfpoiPWt0JEkSVJfVbWtFHubumylmBKrDzgQ5s+nY+IkBn39Yth999rWIEmSpJra2FaKRbkgtH+KYMDMGTBiBAO22abe1UiSJKnODOf1tu++9a5AkiRJBeG2H5IkSVJBGM4lSZKkgjCcS5IkSQVhOJckSZIKwnAuSZIkFYThXJIkSSoIw7kkSZJUEIZzSZIkqSAM55IkSVJBGM6lzbS4rZ1n317K4rb2epciSZL6iAH1LkDqjR6YO5/JM+fRUCrRUS4zZfxITho1rN5lSZKkXs6Zc6mHFre1M3nmPFZ2lFnWvpqVHWUumTnPGXRJkrTFDOdSD7W0rqChtPavTkOpREvrijpVJEmS+grDudRDw4cMoqNcXquto1xm+JBBdapIkiT1FYZzqYeamxqZMn4kAxtKbNs4gIENJaaMH0lzU2O9S5MkSb2cF4RKm+GkUcMYu+dQWlpXMHzIIIO5JEmqCMO5tJmamxoN5ZIkqaJc1iJJkiQVhOFckiRJKgjDuSRJklQQhnNJkiSpIAznkiRJUkEYziVJkqSCMJxLkiRJBWE4V9Usbmvn2beXsritvd6lSJIk9QrehEhV8cDc+UyeOY+GUomOcpkp40dy0qhh9S5LkiSp0Jw5V8Utbmtn8sx5rOwos6x9NSs7ylwyc54z6JIkSZtgOFfFtbSuoKG09tBqKJVoaV2x2ed0iYwkSeoPXNaiihs+ZBAd5fJabR3lMsOHDNqs87lERpIk9RfOnKvimpsamTJ+JAMbSmzbOICBDSWmjB9Jc1Njj8/lEhlJktSfOHOuqjhp1DDG7jmUltYVDB8yaLOCOXy4RGYlH87Edy6R2dxzSpIkFZXhXFXT3NS4xQG60ktkJEmSisxlLSq0Si6RAS8slSRJxebMuQqvUktkvLBUkiQVneFcvcKWLpHpemFp5/r1S2bOY+yeQ127LkmSCsNlLeoXqrH3uiRJUqUZztUveGGpJEnqDQzn6hcqfWGpJElSNbjmXP1GpS4slSRJqhbDufqVSuy9LkmSVC0ua5EkSZIKwnAuSZIkFYThXJIkSSoIw7kkSZJUEIZzSZIkqSAM55IkSVJBGM4lSZKkgjCcS5IkSQVhOJckSZIKwnAuSZIkFUSklOpdQyFExP8Ab9bpxw8F3q3Tz+6P7O/asr9rzz6vLfu7tuzv2rK/q2O3lNLH1/eC4bwAImJOSml0vevoL+zv2rK/a88+ry37u7bs79qyv2vPZS2SJElSQRjOJUmSpIIwnBfD1HoX0M/Y37Vlf9eefV5b9ndt2d+1ZX/XmGvOJUmSpIJw5lySJEkqCMN5FUTELRGxKCKe79J2UEQ8HRHPRcSPImK7vL0hIqbl7S9GxDe6HPOpvP21iPh+REQ9Pk9vUME+fzwiXo6Iufljx3p8nqLrYX9vHRG35u3PRsRRXY5xjHdDBfvb8d0NEbFrRDyW//fhhYj4Wt6+Q0Q8EhGv5n8O6XLMN/Jx/HJEHNel3TG+CRXub8f4JvS0vyOiOX9/W0Rcu865HN/VkFLyUeEHcCRwCPB8l7ZfAZ/Nn38FuCJ/fgZwd/58G+B3wCfyv88GDgMCeBg4vt6fraiPCvb548Doen+eoj962N8XArfmz3cEngFK+d8d47Xtb8d39/p7Z+CQ/Pm2wCvAfsAU4NK8/VLgqvz5fsCzQCOwO/BbYKv8Ncd4bfvbMV75/h4MfAb4KnDtOudyfFfh4cx5FaSUngCWrNO8D/BE/vwRYHzn24HBETEAGASsAt6PiJ2B7VJKT6fsN2A6cErVi++lKtHntaizr+hhf+8H/Cw/bhGwFBjtGO++SvR3DcrsM1JKC1JKv86fLwNeBIYBJwPT8rdN48PxejLZP/jbU0pvAK8BYxzj3VOp/q5t1b1XT/s7pbQ8pfQksLLreRzf1WM4r53ngZPy56cDu+bPZwDLgQXAW8C/pJSWkP2itHQ5viVvU/f1tM873Zp/HXqZX9H1yIb6+1ng5IgYEBG7A5/KX3OMb5me9ncnx3cPRMQngIOBWcBOKaUFkAUcsm8mIBu3b3c5rHMsO8Z7aAv7u5NjvJu62d8b4viuEsN57XwFuDAiniH7GmlV3j4GWAPsQvb13N9ExB5kXxGty611eqanfQ5wZkrpQOCI/PG/a1tyr7ah/r6F7D/ac4CrgV8Cq3GMb6me9jc4vnskIpqAmcDXU0ob+3ZtQ2PZMd4DFehvcIx3Ww/6e4OnWE+b47sCBtS7gP4ipfQScCxAROwNfD5/6QzgJymlDmBRRDxF9hX0L4DhXU4xHHindhX3fpvR56+nlObnxy6LiDvJgvz0mhffC22ov1NKq4G/6nxfRPwSeBVoxTG+2Tajv3F8d19ENJAFl39LKd2fNy+MiJ1TSgvyr/QX5e0trP3tROdYbsEx3i0V6m/HeDf1sL83xPFdJc6c10jnFeMRUQL+AfhB/tJbwOciMxg4FHgp/0ppWUQcmn8tdzbwQB1K77V62uf5MoCh+TENwAlkSwfUDRvq74jYJu9nIuJPgdUppd84xrdMT/vb8d19+Xi8GXgxpfTdLi89CEzMn0/kw/H6IDAhIhrzpUR7AbMd491Tqf52jHfPZvT3ejm+q8ebEFVBRNwFHAUMBRYClwNNZLsoANwPfCOllPKvlW4lu4gryHZZ+HZ+ntHAbWQXLT4MXJz8H2y9KtHneaB5AmgAtgIeBf46pbSmlp+lN+hhf38C+E+gDMwHzkkpvZmfxzHeDZXob8d390XEZ8i+vXyOrB8B/o5sXe69wAiyf+Sf3nm9SkT8PdlSo9VkywQeztsd45tQqf52jHfPZvb374DtgK3JLjI/Nv9Hv+O7CgznkiRJUkG4rEWSJEkqCMO5JEmSVBCGc0mSJKkgDOeSJElSQRjOJUmSpIIwnEuS1pLfA+DJiDi+S9sXI+In9axLkvoDt1KUJH1ERBwA3AccTLZn9FxgXErpt1twzgH5HUwlSRtgOJckrVdETAGWA4OBZSmlKyJiItnNj7YGfglclFIqR8RU4BCym5Hck1L6Zn6OFuBGYBxwdUrpvjp8FEnqNQbUuwBJUmH9I/BrYBUwOp9N/wJweEppdR7IJwB3ApemlJZExADgsYiYkVL6TX6e5SmlsfX4AJLU2xjOJUnrlVJaHhH3AG0ppfaIOAb4NDAnIiCbJX87f/uXI+Icsv9f2QXYD+gM5/fUtnJJ6r0M55KkjSnnD4AAbkkpXdb1DRGxF/A1YExKaWlE3AEM7PKW5TWpVJL6AHdrkSR116PAFyNiKEBENEfECGA7YBnwfkTsDBxXxxolqVdz5lyS1C0ppeci4h+BRyOiBHQAXwXmkC1heR54HXiqflVKUu/mbi2SJElSQbisRZIkSSoIw7kkSZJUEIZzSZIkqSAM55IkSVJBGM4lSZKkgjCcS5IkSQVhOJckSZIKwnAuSZIkFcT/B29KnhCdmX0+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data points\n",
    "df_total.plot(kind='scatter',\n",
    "              x='year',\n",
    "              y='total',\n",
    "              figsize=(12, 6)\n",
    "             )\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Regression with Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980.0</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981.0</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982.0</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983.0</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984.0</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year   total\n",
       "0  1980.0   99137\n",
       "1  1981.0  110563\n",
       "2  1982.0  104271\n",
       "3  1983.0   75550\n",
       "4  1984.0   73417"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_total = pd.DataFrame(df_canada[years].sum(axis=0))\n",
    "\n",
    "# change the years to type float (useful for regression later on)\n",
    "df_total.index = map(float, df_total.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.regplot(x='year', y='total', data=df_total, color='green', marker='+')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot LOESS curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.regplot(x='year', y='total', data=df_total, lowess=True, color='green', marker='+')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Correlation (3 variables)\n",
    "\n",
    "### Arrange coutry wise yearly data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  Venezuela  \\\n",
       "0                            9378      128           0        0        103   \n",
       "1                           10030      132           0        0        117   \n",
       "2                            9074      146           0        0        174   \n",
       "3                            7100      105           0        0        124   \n",
       "4                            6661       90           0        0        142   \n",
       "\n",
       "Country  Vietnam  Western Sahara  Yemen  Zambia  Zimbabwe  \n",
       "0           1191               0      1      11        72  \n",
       "1           1829               0      2      17       114  \n",
       "2           2162               0      1      11       102  \n",
       "3           3404               0      6       7        44  \n",
       "4           7583               0      0      16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transposed dataframe \n",
    "df_can_t = df_canada[years].transpose()\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute normalized immigration data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     0.087586\n",
      "1     0.125557\n",
      "2     0.043379\n",
      "3     0.008184\n",
      "4     0.000000\n",
      "5     0.007039\n",
      "6     0.010546\n",
      "7     0.027182\n",
      "8     0.029983\n",
      "9     0.068100\n",
      "10    0.159510\n",
      "11    0.310008\n",
      "12    0.226977\n",
      "13    0.201914\n",
      "14    0.282558\n",
      "15    0.313491\n",
      "16    0.435687\n",
      "17    0.461505\n",
      "18    0.475485\n",
      "19    0.695180\n",
      "20    0.828166\n",
      "21    0.850208\n",
      "22    0.741262\n",
      "23    0.850330\n",
      "24    0.854714\n",
      "25    1.000000\n",
      "26    0.779185\n",
      "27    0.636067\n",
      "28    0.694400\n",
      "29    0.684293\n",
      "30    0.703023\n",
      "31    0.657013\n",
      "32    0.767153\n",
      "33    0.794067\n",
      "Name: China, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# normalize China data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "\n",
    "print(norm_china)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize using Bubble Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f49fb071048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(15, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 500 + 10,  # pass in weights as size\n",
    "                    xlim=(1980, 2013)\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China')\n",
    "ax0.legend(['China'], loc='upper left', fontsize='x-large')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
